Publication-only abstracts (abstract number preceded by an "e"), published in conjunction with the 2019 ASCO Annual Meeting but not presented at the Meeting, can be found online only.
Development and validation of a deep learning model to assess tumor progression to immunotherapy.
Metastatic Non-Small Cell Lung Cancer
Lung Cancer—Non-Small Cell Metastatic
2019 ASCO Annual Meeting
J Clin Oncol 37, 2019 (suppl; abstr e20601)
Author(s): Yi Yang, Jiancheng Yang, Yuxiang Ye, Tian Xia, Shun Lu; Shanghai Chest Hospital, Shanghai, China; Department of Electronic Engineering, Shanghai Jiao Tong University/Diannei Technology/Diannei Technology, Shanghai, China; Shanghai Diannei Technology, Shanghai, China; Department of Oncology, Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai JiaoTong University, Shanghai, China
Background: Manual application of length-based tumor response criteria is the standard-of-care for assessing metastatic tumor response. It is technically challenging, time-consuming and associated with low reproducibility. In this study, we presented a novel automatic Deep Neural Networks (DNNs) based segmentation method for assessing tumor progression to immunotherapy. Next stage, AI will assist Physicians assessing pseudo-progression. Methods: A data set of 39 lung cancer patients with 156 computed tomography (CT) scans was used for model training and validation. A 3D segmentation DNN DenseSharp, was trained with an input size of on CT scans of tumor with manual delineated volume of interest (VOI) as ground truth. The trained model was subsequently used to estimate the volumes of target lesions via 16 sliding windows. We referred the progression-free survival (PFS) only considering tumor size as PFS-T. PFS-Ts assessed by longest tumor diameter (PFS-Tdiam), by tumor volume (PFS-Tvol), and by predicted tumor volume (PFS-Tpred-vol) were compared with standard PFS (as assessed by one junior and one senior clinician). Tumor progression was defined as > 20% increase in the longest tumor diameter or > 50% increase in tumor volume. Effective treatment was defined as a PFS of > 60 days after immunotherapy. Results: In a 4-fold cross-validation test, the DenseSharp segmentation neural network achieved a mean per-class intersection over union (mIoU) of 80.1%. The effectiveness rates of immunotherapy assessed using PFS-Tdiam (32 / 39, 82.1%), PFS-Tvol (33/39, 84.6%) and PFS-T pred-vol (32/39, 82.1%) were the same as standard PFS. The agreement between PFS-Tvol, and PFS-Tpred-vol was 97.4% (38/39). Evaluation time with deep learning model implemented with PyTorch 0.4.1 on GTX 1080 GPU was hundred-fold faster than manual evaluation (1.42s vs. 5-10 min per patient). Conclusions: In this study, DNN based model demonstrated fast and stable performance for tumor progression evaluation. Automatic volumetric measurement of tumor lesion enabled by deep learning provides the potential for a more efficient, objective and sensitive measurement than linear measurement by clinicians.